Arguments

If TRUE, level is set to seq(51,99,by=3). This is suitable for
fan plots.

lambda

Box-Cox transformation parameter. If lambda="auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.

biasadj

Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted values,
a regular back transformation will result in median forecasts. If biasadj is TRUE,
an adjustment will be made to produce mean forecasts and fitted values.

method

Method for selecting the smoothing parameter. If
method="gcv", the generalized cross-validation method from
smooth.spline is used. If method="mle", the
maximum likelihood method from Hyndman et al (2002) is used.

x

Deprecated. Included for backwards compatibility.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the
results, while the function plot produces a plot of the forecasts and
prediction intervals.

The generic accessor functions fitted.values and residuals
extract useful features of the value returned by splinef.

An object of class "forecast" containing the following elements:

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

mean

Point forecasts as a time series

lower

Lower limits for
prediction intervals

upper

Upper limits for prediction intervals

level

The confidence values associated with the prediction intervals

x

The original time series (either object itself or the time
series used to create the model stored as object).

onestepf

One-step forecasts from the fitted model.

fitted

Smooth estimates of the fitted trend using all data.

residuals

Residuals from the fitted model. That is x minus one-step
forecasts.

Details

The cubic smoothing spline model is equivalent to an ARIMA(0,2,2) model but
with a restricted parameter space. The advantage of the spline model over
the full ARIMA model is that it provides a smooth historical trend as well
as a linear forecast function. Hyndman, King, Pitrun, and Billah (2002) show
that the forecast performance of the method is hardly affected by the
restricted parameter space.